






"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCamelCase_							      = logging.getLogger(__name__)

def 							snake_case (  ):
			UpperCAmelCase_	:					Union[str, Any]     	=   argparse.ArgumentParser(
			    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."		)
			parser.add_argument("--file_path"      ,type=A__      ,default="data/dump.txt"      ,help="The path to the data."		)
			parser.add_argument("--tokenizer_type"      ,type=A__      ,default="bert"      ,choices=["bert", "roberta", "gpt2"]		)
			parser.add_argument("--tokenizer_name"      ,type=A__      ,default="bert-base-uncased"      ,help="The tokenizer to use."		)
			parser.add_argument("--dump_file"      ,type=A__      ,default="data/dump"      ,help="The dump file prefix."		)
			UpperCAmelCase_	:					Dict     	=   parser.parse_args()

			logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""		)
			if args.tokenizer_type == "bert":
						UpperCAmelCase_	:					Any     	=   BertTokenizer.from_pretrained(args.tokenizer_name		)
						UpperCAmelCase_	:					Tuple     	=   tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
						UpperCAmelCase_	:					str     	=   tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						UpperCAmelCase_	:					Optional[Any]     	=   RobertaTokenizer.from_pretrained(args.tokenizer_name		)
						UpperCAmelCase_	:					List[Any]     	=   tokenizer.special_tokens_map["cls_token"]  # `<s>`
						UpperCAmelCase_	:					int     	=   tokenizer.special_tokens_map["sep_token"]  # `</s>`
			elif args.tokenizer_type == "gpt2":
						UpperCAmelCase_	:					Optional[int]     	=   GPTaTokenizer.from_pretrained(args.tokenizer_name		)
						UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
						UpperCAmelCase_	:					Dict     	=   tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

			logger.info(F"""Loading text from {args.file_path}"""		)
			with open(args.file_path      ,"r"      ,encoding="utf8"		) as fp:
						UpperCAmelCase_	:					Any     	=   fp.readlines()

			logger.info("Start encoding"		)
			logger.info(F"""{len(A__		)} examples to process."""		)

			UpperCAmelCase_	:					List[Any]     	=   []
			UpperCAmelCase_	:					Any     	=   0
			UpperCAmelCase_	:					Optional[int]     	=   1_00_00
			UpperCAmelCase_	:					Dict     	=   time.time()
			for text in data:
						UpperCAmelCase_	:					Union[str, Any]     	=   F"""{bos} {text.strip()} {sep}"""
						UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.encode(A__      ,add_special_tokens=A__		)
						rslt.append(A__		)

						iter += 1
						if iter % interval == 0:
									UpperCAmelCase_	:					Optional[int]     	=   time.time()
									logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""		)
									UpperCAmelCase_	:					str     	=   time.time()
			logger.info("Finished binarization"		)
			logger.info(F"""{len(A__		)} examples processed."""		)

			UpperCAmelCase_	:					str     	=   F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
			UpperCAmelCase_	:					Dict     	=   tokenizer.vocab_size
			if vocab_size < (1 << 16):
						UpperCAmelCase_	:					int     	=   [np.uintaa(A__		) for d in rslt]
			else:
						UpperCAmelCase_	:					str     	=   [np.intaa(A__		) for d in rslt]
			random.shuffle(rslt_		)
			logger.info(F"""Dump to {dp_file}"""		)
			with open(A__      ,"wb"		) as handle:
						pickle.dump(rslt_      ,A__      ,protocol=pickle.HIGHEST_PROTOCOL		)


if __name__ == "__main__":
			main()






import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase			:				List[str]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_=None     ,				lowerCamelCase_=None		):
							return field(default_factory=lambda: default     ,				metadata=lowerCamelCase_		)







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		List[str]								=	list_field(
							    default=[]		,       metadata={
							        '''help''': (
							            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
							            ''' of all available models'''
							        )
							    }		,       )

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8]		,       metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}				)

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8, 32, 1_28, 5_12]		,       metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}		,       )

							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Use FP16 to accelerate inference.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Benchmark training of model'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Verbose memory tracing'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
							    }		,       )
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Trace memory line by line'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save result to a CSV file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save all print statements in a log file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to print environment information'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
							            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
							            ''' for debugging / testing and on TPU.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'env_info_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving environment information.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'log_{round(time()				)}.csv'		,       metadata={'''help''': '''Log filename used if print statements are saved in log.'''}		,       )
							UpperCAmelCase_						:		int								=	field(default=3		,       metadata={'''help''': '''Times an experiment will be run.'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
							            ''' model weights.'''
							        )
							    }		,       )




							def       snake_case			(					self						)		->       Union[str, Any]:
														warnings.warn(
														    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
														    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
														    ''' to benchmark Transformer models.'''    ,						__UpperCAmelCase    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														return json.dumps(dataclasses.asdict(self						)    ,						indent=2						)




							@property
							def       snake_case			(					self						)		->       List[str]:
														if len(self.models						) <= 0:
																					raise ValueError(
																					    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																					    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''						)
														return self.models






							@property
							def       snake_case			(					self						)		->       int:
														if not self.multi_process:
																					return False
														elif self.is_tpu:
																					logger.info('''Multiprocessing is currently not possible on TPU.'''						)
																					return False
														else:
																					return True






